Many deep learning methods have been applied to address general audio synthesis. Autoregressive models are some of the most influential, achieving state-of-the-art results in many audio synthesis tasks, particularly for speech generation [1]}. However, autoregressive methods are generally slow at generation and afford little control, essential in a music creation tool. Latent variable models such as Generative Adversarial Networks (GANs) [2]}, or Variational Auto-Encoders (VAEs) [3]}, have been more widely used in this sense as they are faster and allow manipulating learned controls affecting high-level factors of variation in the generated data [4]}, [5]}, [6]}. Specifically, GANs have shown promising results in drum sound synthesis [7]}, [8]}, [9]} and are generally superior to other generative methods in terms of speed and quality. Recently, Denoising Diffusion models have shown results on par with GANs [10]} and were applied to drum sound synthesis obtaining unprecedented audio quality and diversity [11]}. However, diffusion models require an iterative denoising process that is often time-consuming.
